EEG Signals Classification by Using an Ensemble TPUnit Neural Networks for the Diagnosis of Epilepsy

Abstract—The electroencephalogram (EEG) is necessary for
the diagnosis of epilepsy. To make a diagnosis of epilepsy
exactly, a full EEG recording for a long stretch of time is needed.
The observation for a long record is a big burden for a doctor.
To reduce this burden, a computer aid is important. This paper
presented classifications of EEG patterns using the ensemble
TPunit NNs for the diagnosis of epilepsy. The classification
accuracy rates of the proposed classifiers were found to be
higher than that of stand alone neural network. In addition, the
classification accuracy was higher than previous study. The
ensemble of the TPUnit neural networks is highly effective in
classification problem.